{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gPuN7cmcL9cv"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/03-langchain-conversational-memory.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/03-langchain-conversational-memory.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ytU8AV9uMty3"
      },
      "source": [
        "#### [LangChain Handbook](https://www.pinecone.io/learn/series/langchain/)\n",
        "\n",
        "# Conversational Memory\n",
        "\n",
        "## Extra Material: Token Counter\n",
        "\n",
        "This is an additional piece of material alongside the [LangChain Handbook notebook on Conversational Memory](https://github.com/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/03-langchain-conversational-memory.ipynb).\n",
        "\n",
        "In this notebook we will count the number of tokens used in a conversation for different conversational memory types.\n",
        "\n",
        "We begin by installing the required libraries:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zwS2JId1L9cv",
        "outputId": "9a7c88d4-3e2f-4187-8b77-acda30622174"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.0 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.3/1.0 MB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m18.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m14.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/2.5 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m91.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m47.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.3/65.3 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m363.0/363.0 kB\u001b[0m \u001b[31m20.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.2/45.2 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.9/50.9 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -qU \\\n",
        "  langchain==0.3.25 \\\n",
        "  langchain-community==0.3.25 \\\n",
        "  langchain-openai==0.3.22 \\\n",
        "  transformers==4.52.4 \\\n",
        "  seaborn==0.13.2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2zWvthQzUUzC"
      },
      "source": [
        "To run the notebook we'll use OpenAI's `gpt-4.1-mini` model. We initialize it via LangChain like so:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvimTSA4Ublb",
        "outputId": "d600eb02-603c-448a-e223-8cb17974e8f5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your OpenAI API key: ··········\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "from getpass import getpass\n",
        "\n",
        "os.environ[\"OPENAI_API_KEY\"] = os.getenv(\"OPENAI_API_KEY\") \\\n",
        "    or getpass(\"Enter your OpenAI API key: \")\n",
        "\n",
        "OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "oRjdCaSaUdCd"
      },
      "outputs": [],
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "llm = ChatOpenAI(\n",
        "    temperature=0,\n",
        "    openai_api_key=OPENAI_API_KEY,\n",
        "    model_name='gpt-4.1-mini'\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fUv5H5SWUgNt"
      },
      "source": [
        "To count the number of tokens used during each call we will define a `count_tokens` function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "EqTuGsENUnAr"
      },
      "outputs": [],
      "source": [
        "from langchain.callbacks import get_openai_callback\n",
        "\n",
        "def count_tokens(chain, query, config=None):\n",
        "    with get_openai_callback() as cb:\n",
        "        # Handle both dict and string inputs\n",
        "        if isinstance(query, str):\n",
        "            query = {\"query\": query}\n",
        "\n",
        "        # Use provided config or default\n",
        "        if config is None:\n",
        "            config = {\"configurable\": {\"session_id\": \"default\"}}\n",
        "\n",
        "        result = chain.invoke(query, config=config)\n",
        "        print(f'Spent a total of {cb.total_tokens} tokens')\n",
        "\n",
        "    return {\n",
        "        'result': result,\n",
        "        'token_count': cb.total_tokens\n",
        "    }"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wH3HtA2oL9cw"
      },
      "source": [
        "## Define System Prompt and LCEL Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "terrcggzL9cw"
      },
      "outputs": [],
      "source": [
        "from langchain.prompts import (\n",
        "    ChatPromptTemplate,\n",
        "    SystemMessagePromptTemplate,\n",
        "    HumanMessagePromptTemplate,\n",
        "    MessagesPlaceholder\n",
        ")\n",
        "from langchain.schema.output_parser import StrOutputParser\n",
        "\n",
        "# Define the prompt template\n",
        "system_prompt = \"\"\"The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\"\"\"\n",
        "\n",
        "prompt_template = ChatPromptTemplate.from_messages([\n",
        "    SystemMessagePromptTemplate.from_template(system_prompt),\n",
        "    MessagesPlaceholder(variable_name=\"history\"),\n",
        "    HumanMessagePromptTemplate.from_template(\"{query}\"),\n",
        "])\n",
        "\n",
        "# Create the LCEL pipeline\n",
        "output_parser = StrOutputParser()\n",
        "pipeline = prompt_template | llm | output_parser"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQHFpyi3L9cw"
      },
      "source": [
        "## Runnables with Message Histories"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "blRPHXkJL9cx"
      },
      "source": [
        "Here the Runnable Classes which utilize different types of memory are defined.\n",
        "\n",
        "### Memory Type #1: Buffer Memory - Store the Entire Chat History\n",
        "\n",
        "An alternative to `ConversationBufferMemory`. The simplest method, which stores the entire chat history as memory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "iGPU-bOaL9cx"
      },
      "outputs": [],
      "source": [
        "from langchain_core.chat_history import InMemoryChatMessageHistory\n",
        "\n",
        "# Create a simple chat history storage\n",
        "chat_map = {}\n",
        "\n",
        "def get_chat_history(session_id: str) -> InMemoryChatMessageHistory:\n",
        "    if session_id not in chat_map:\n",
        "        # if session ID doesn't exist, create a new chat history\n",
        "        chat_map[session_id] = InMemoryChatMessageHistory()\n",
        "    return chat_map[session_id]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m21c0CNYL9cx"
      },
      "source": [
        "### Memory type #2: Summary - Store Summaries of Past Interactions\n",
        "\n",
        "This is an LCEL-Comptaible alternative to `ConversationSummaryMemory`. We keep a summary of our previous conversation snippets as our history. The summarization is performed by an LLM."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "CdFI6MEXL9cx"
      },
      "outputs": [],
      "source": [
        "from langchain_core.messages import BaseMessage, SystemMessage\n",
        "\n",
        "from langchain_core.chat_history import BaseChatMessageHistory\n",
        "from pydantic import BaseModel, Field\n",
        "\n",
        "class ConversationSummaryMessageHistory(BaseChatMessageHistory, BaseModel):\n",
        "    messages: list[BaseMessage] = Field(default_factory=list)\n",
        "    llm: ChatOpenAI = Field(default_factory=ChatOpenAI)\n",
        "\n",
        "    def __init__(self, llm: ChatOpenAI):\n",
        "        super().__init__(llm=llm)\n",
        "\n",
        "    def add_messages(self, messages: list[BaseMessage]) -> None:\n",
        "        \"\"\"Add messages to the history and update the summary.\"\"\"\n",
        "        self.messages.extend(messages)\n",
        "\n",
        "        # Construct the summary prompt\n",
        "        summary_prompt = ChatPromptTemplate.from_messages([\n",
        "            SystemMessagePromptTemplate.from_template(\n",
        "                \"Given the existing conversation summary and the new messages, \"\n",
        "                \"generate a new summary of the conversation. Ensure to maintain \"\n",
        "                \"as much relevant information as possible.\"\n",
        "            ),\n",
        "            HumanMessagePromptTemplate.from_template(\n",
        "                \"Existing conversation summary:\\n{existing_summary}\\n\\n\"\n",
        "                \"New messages:\\n{messages}\"\n",
        "            )\n",
        "        ])\n",
        "\n",
        "        # Format the messages and invoke the LLM\n",
        "        new_summary = self.llm.invoke(\n",
        "            summary_prompt.format_messages(\n",
        "                existing_summary=self.messages,\n",
        "                messages=messages\n",
        "            )\n",
        "        )\n",
        "\n",
        "        # Replace the existing history with a single system summary message\n",
        "        self.messages = [SystemMessage(content=new_summary.content)]\n",
        "\n",
        "    def clear(self) -> None:\n",
        "        \"\"\"Clear the history.\"\"\"\n",
        "        self.messages = []\n",
        "\n",
        "# Create get_summary_chat_history function for summary memory\n",
        "summary_chat_map = {}\n",
        "\n",
        "def get_summary_chat_history(session_id: str, llm: ChatOpenAI) -> ConversationSummaryMessageHistory:\n",
        "    if session_id not in summary_chat_map:\n",
        "        summary_chat_map[session_id] = ConversationSummaryMessageHistory(llm=llm)\n",
        "    return summary_chat_map[session_id]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0U4aYuOeL9cx"
      },
      "source": [
        "### Memory type #3: Window Buffer Memory - Keep Latest Interactions\n",
        "\n",
        "An LCEL-compatible alternative to `ConversationBufferWindowMemory`. Window memory where we keep only the last k interactions in our memory and intentionally drop the oldest ones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "vJ5-LUOlL9cx"
      },
      "outputs": [],
      "source": [
        "class BufferWindowMessageHistory(BaseChatMessageHistory, BaseModel):\n",
        "    messages: list[BaseMessage] = Field(default_factory=list)\n",
        "    k: int = Field(default_factory=int)\n",
        "\n",
        "    def __init__(self, k: int):\n",
        "        super().__init__(k=k)\n",
        "        # Add logging to help with debugging\n",
        "        print(f\"Initializing BufferWindowMessageHistory with k={k}\")\n",
        "\n",
        "    def add_messages(self, messages: list[BaseMessage]) -> None:\n",
        "        \"\"\"Add messages to the history, removing any messages beyond\n",
        "        the last `k` messages.\n",
        "        \"\"\"\n",
        "        self.messages.extend(messages)\n",
        "        # Add logging to help with debugging\n",
        "        if len(self.messages) > self.k:\n",
        "            print(f\"Truncating history from {len(self.messages)} to {self.k} messages\")\n",
        "        self.messages = self.messages[-self.k:]\n",
        "\n",
        "    def clear(self) -> None:\n",
        "        \"\"\"Clear the history.\"\"\"\n",
        "        self.messages = []\n",
        "\n",
        "# Create get_chat_history function for window memory\n",
        "window_chat_map = {}\n",
        "\n",
        "def get_window_chat_history(session_id: str, k: int = 4) -> BufferWindowMessageHistory:\n",
        "    print(f\"get_window_chat_history called with session_id={session_id} and k={k}\")\n",
        "    if session_id not in window_chat_map:\n",
        "        window_chat_map[session_id] = BufferWindowMessageHistory(k=k)\n",
        "    return window_chat_map[session_id]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QyU-lr-2L9cx"
      },
      "source": [
        "### Memory type #4:  Window + Summary Hybrid\n",
        "\n",
        "An LCEL-compatible alternative to `ConversationSummaryBufferMemory`. Combines the benefits of both summary and buffer window memory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "2vBeBpE1L9cx"
      },
      "outputs": [],
      "source": [
        "class ConversationSummaryBufferMessageHistory(BaseChatMessageHistory, BaseModel):\n",
        "    messages: list[BaseMessage] = Field(default_factory=list)\n",
        "    llm: ChatOpenAI = Field(default_factory=ChatOpenAI)\n",
        "    k: int = Field(default_factory=int)\n",
        "\n",
        "    def __init__(self, llm: ChatOpenAI, k: int):\n",
        "        super().__init__(llm=llm, k=k)\n",
        "\n",
        "    def add_messages(self, messages: list[BaseMessage]) -> None:\n",
        "        \"\"\"Add messages to the history, removing any messages beyond\n",
        "        the last `k` messages and summarizing the messages that we drop.\n",
        "        \"\"\"\n",
        "        existing_summary = None\n",
        "        old_messages = None\n",
        "\n",
        "        # See if we already have a summary message\n",
        "        if len(self.messages) > 0 and isinstance(self.messages[0], SystemMessage):\n",
        "            existing_summary = self.messages.pop(0)\n",
        "\n",
        "        # Add the new messages to the history\n",
        "        self.messages.extend(messages)\n",
        "\n",
        "        # Check if we have too many messages\n",
        "        if len(self.messages) > self.k:\n",
        "            # Pull out the oldest messages...\n",
        "            old_messages = self.messages[:-self.k]\n",
        "            # ...and keep only the most recent messages\n",
        "            self.messages = self.messages[-self.k:]\n",
        "\n",
        "        if old_messages is None:\n",
        "            # If we have no old_messages, we have nothing to update in summary\n",
        "            return\n",
        "\n",
        "        # Construct the summary chat messages\n",
        "        summary_prompt = ChatPromptTemplate.from_messages([\n",
        "            SystemMessagePromptTemplate.from_template(\n",
        "                \"Given the existing conversation summary and the new messages, \"\n",
        "                \"generate a new summary of the conversation. Ensure to maintain \"\n",
        "                \"as much relevant information as possible.\"\n",
        "            ),\n",
        "            HumanMessagePromptTemplate.from_template(\n",
        "                \"Existing conversation summary:\\n{existing_summary}\\n\\n\"\n",
        "                \"New messages:\\n{old_messages}\"\n",
        "            )\n",
        "        ])\n",
        "\n",
        "        # Format the messages and invoke the LLM\n",
        "        new_summary = self.llm.invoke(\n",
        "            summary_prompt.format_messages(\n",
        "                existing_summary=existing_summary or \"No previous summary\",\n",
        "                old_messages=old_messages\n",
        "            )\n",
        "        )\n",
        "\n",
        "        # Prepend the new summary to the history\n",
        "        self.messages = [SystemMessage(content=new_summary.content)] + self.messages\n",
        "\n",
        "    def clear(self) -> None:\n",
        "        \"\"\"Clear the history.\"\"\"\n",
        "        self.messages = []\n",
        "\n",
        "# Create get_chat_history function for summary buffer memory\n",
        "summary_buffer_chat_map = {}\n",
        "\n",
        "def get_summary_buffer_chat_history(session_id: str, llm: ChatOpenAI, k: int = 4) -> ConversationSummaryBufferMessageHistory:\n",
        "    if session_id not in summary_buffer_chat_map:\n",
        "        summary_buffer_chat_map[session_id] = ConversationSummaryBufferMessageHistory(llm=llm, k=k)\n",
        "    return summary_buffer_chat_map[session_id]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xVKm61B8L9cx"
      },
      "source": [
        "## Create Conversation Chains and Conversation Function"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xprpc4qGC0aS"
      },
      "source": [
        "Create set of conversation chains that we'll be using:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "I53dXttjDCgA"
      },
      "outputs": [],
      "source": [
        "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
        "from langchain_core.runnables import ConfigurableFieldSpec\n",
        "\n",
        "# First, create separate history factory functions for each k value\n",
        "def get_window_chat_history_k6(session_id: str, k: int = 6):\n",
        "    return BufferWindowMessageHistory(k=k)  # Changed from WindowChatHistory\n",
        "\n",
        "def get_window_chat_history_k12(session_id: str, k: int = 12):\n",
        "    return BufferWindowMessageHistory(k=k)  # Changed from WindowChatHistory\n",
        "\n",
        "def get_summary_buffer_chat_history_k6(session_id: str, llm: ChatOpenAI, k: int = 6):\n",
        "    return ConversationSummaryBufferMessageHistory(llm=llm, k=k)  # Changed from SummaryBufferChatHistory\n",
        "\n",
        "def get_summary_buffer_chat_history_k12(session_id: str, llm: ChatOpenAI, k: int = 12):\n",
        "    return ConversationSummaryBufferMessageHistory(llm=llm, k=k)  # Changed from SummaryBufferChatHistory\n",
        "\n",
        "# Then update the conversation_chains dictionary to use these specific functions\n",
        "conversation_chains = {\n",
        "    'RunnableWithMessageHistory': RunnableWithMessageHistory(\n",
        "        pipeline,\n",
        "        get_session_history=get_chat_history,\n",
        "        input_messages_key=\"query\",\n",
        "        history_messages_key=\"history\"\n",
        "    ),\n",
        "    'ConversationSummaryMessageHistory': RunnableWithMessageHistory(\n",
        "        pipeline,\n",
        "        get_session_history=get_summary_chat_history,\n",
        "        input_messages_key=\"query\",\n",
        "        history_messages_key=\"history\",\n",
        "        history_factory_config=[\n",
        "            ConfigurableFieldSpec(\n",
        "                id=\"session_id\",\n",
        "                annotation=str,\n",
        "                name=\"Session ID\",\n",
        "                description=\"The session ID to use for the chat history\",\n",
        "                default=\"summary_history\",\n",
        "            ),\n",
        "            ConfigurableFieldSpec(\n",
        "                id=\"llm\",\n",
        "                annotation=ChatOpenAI,\n",
        "                name=\"LLM\",\n",
        "                description=\"The LLM to use for the conversation summary\",\n",
        "                default=llm,\n",
        "            )\n",
        "        ]\n",
        "    ),\n",
        "    'BufferWindowMessageHistory(k=6)': RunnableWithMessageHistory(\n",
        "        pipeline,\n",
        "        get_session_history=get_window_chat_history_k6,  # Changed to k6 specific function\n",
        "        input_messages_key=\"query\",\n",
        "        history_messages_key=\"history\",\n",
        "        history_factory_config=[\n",
        "            ConfigurableFieldSpec(\n",
        "                id=\"session_id\",\n",
        "                annotation=str,\n",
        "                name=\"Session ID\",\n",
        "                description=\"The session ID to use for the chat history\",\n",
        "                default=\"window_history_k6\",\n",
        "            ),\n",
        "            ConfigurableFieldSpec(\n",
        "                id=\"k\",\n",
        "                annotation=int,\n",
        "                name=\"k\",\n",
        "                description=\"The number of messages to keep in the history\",\n",
        "                default=6,\n",
        "            )\n",
        "        ]\n",
        "    ),\n",
        "    'BufferWindowMessageHistory(k=12)': RunnableWithMessageHistory(\n",
        "        pipeline,\n",
        "        get_session_history=get_window_chat_history_k12,  # Changed to k12 specific function\n",
        "        input_messages_key=\"query\",\n",
        "        history_messages_key=\"history\",\n",
        "        history_factory_config=[\n",
        "            ConfigurableFieldSpec(\n",
        "                id=\"session_id\",\n",
        "                annotation=str,\n",
        "                name=\"Session ID\",\n",
        "                description=\"The session ID to use for the chat history\",\n",
        "                default=\"window_history_k12\",\n",
        "            ),\n",
        "            ConfigurableFieldSpec(\n",
        "                id=\"k\",\n",
        "                annotation=int,\n",
        "                name=\"k\",\n",
        "                description=\"The number of messages to keep in the history\",\n",
        "                default=12,\n",
        "            )\n",
        "        ]\n",
        "    ),\n",
        "    'ConversationSummaryBufferMessageHistory(k=6)': RunnableWithMessageHistory(\n",
        "        pipeline,\n",
        "        get_session_history=get_summary_buffer_chat_history_k6,  # Changed to k6 specific function\n",
        "        input_messages_key=\"query\",\n",
        "        history_messages_key=\"history\",\n",
        "        history_factory_config=[\n",
        "            ConfigurableFieldSpec(\n",
        "                id=\"session_id\",\n",
        "                annotation=str,\n",
        "                name=\"Session ID\",\n",
        "                description=\"The session ID to use for the chat history\",\n",
        "                default=\"summary_buffer_k6\",\n",
        "            ),\n",
        "            ConfigurableFieldSpec(\n",
        "                id=\"llm\",\n",
        "                annotation=ChatOpenAI,\n",
        "                name=\"LLM\",\n",
        "                description=\"The LLM to use for the conversation summary\",\n",
        "                default=llm,\n",
        "            ),\n",
        "            ConfigurableFieldSpec(\n",
        "                id=\"k\",\n",
        "                annotation=int,\n",
        "                name=\"k\",\n",
        "                description=\"The number of messages to keep in the history\",\n",
        "                default=6,\n",
        "            )\n",
        "        ]\n",
        "    ),\n",
        "    'ConversationSummaryBufferMessageHistory(k=12)': RunnableWithMessageHistory(\n",
        "        pipeline,\n",
        "        get_session_history=get_summary_buffer_chat_history_k12,  # Changed to k12 specific function\n",
        "        input_messages_key=\"query\",\n",
        "        history_messages_key=\"history\",\n",
        "        history_factory_config=[\n",
        "            ConfigurableFieldSpec(\n",
        "                id=\"session_id\",\n",
        "                annotation=str,\n",
        "                name=\"Session ID\",\n",
        "                description=\"The session ID to use for the chat history\",\n",
        "                default=\"summary_buffer_k12\",\n",
        "            ),\n",
        "            ConfigurableFieldSpec(\n",
        "                id=\"llm\",\n",
        "                annotation=ChatOpenAI,\n",
        "                name=\"LLM\",\n",
        "                description=\"The LLM to use for the conversation summary\",\n",
        "                default=llm,\n",
        "            ),\n",
        "            ConfigurableFieldSpec(\n",
        "                id=\"k\",\n",
        "                annotation=int,\n",
        "                name=\"k\",\n",
        "                description=\"The number of messages to keep in the history\",\n",
        "                default=12,\n",
        "            )\n",
        "        ]\n",
        "    )\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XSB2VmaNL9cx"
      },
      "source": [
        "Let's define the conversation function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "YDWF27jvL9cx"
      },
      "outputs": [],
      "source": [
        "from tqdm.auto import tqdm\n",
        "import openai\n",
        "\n",
        "queries = [\n",
        "    \"Good morning AI?\",\n",
        "    \"\"\"My interest here is to explore the potential of integrating Large\n",
        "    Language Models with external knowledge\"\"\",\n",
        "    \"I just want to analyze the different possibilities. What can you think of?\",\n",
        "    \"What about the use of retrieval augmentation, can that be used as well?\",\n",
        "    \"\"\"That's very interesting, can you tell me more about this? Like what\n",
        "    systems would I use to store the information and retrieve relevant info?\"\"\",\n",
        "    \"\"\"Okay that's cool, I've been hearing about 'vector databases', are they\n",
        "    relevant in this context?\"\"\",\n",
        "    \"\"\"Okay that's useful, but how do I go from my external knowledge to\n",
        "    creating these 'vectors'? I have no idea how text can become a vector?\"\"\",\n",
        "    \"\"\"Well I don't think I'd be using word embeddings right? If I wanted to\n",
        "    store my documents in this vector database, I suppose I would need to\n",
        "    transform the documents into vectors? Maybe I can use the 'sentence\n",
        "    embeddings' for this, what do you think?\"\"\",\n",
        "    \"\"\"Can sentence embeddings only represent sentences of text? That seems\n",
        "    kind of small to capture any meaning from a document? Is there any approach\n",
        "    that can encode at least a paragraph of text?\"\"\",\n",
        "    \"\"\"Huh, interesting. I do remember reading something about 'mpnet' or\n",
        "    'minilm' sentence 'transformer' models that could encode small to\n",
        "    medium sized paragraphs. Am I wrong about this?\"\"\",\n",
        "    \"\"\"Ah that's great to hear, do you happen to know how much text I can feed\n",
        "    into these types of models?\"\"\",\n",
        "    \"\"\"I've never heard of hierarchical embeddings, could you explain those in\n",
        "    more detail?\"\"\",\n",
        "    \"\"\"So is it like you have a transformer model or something else that creates\n",
        "    sentence level embeddings, then you feed all of the sentence level\n",
        "    embeddings into another separate neural network that knows how to merge\n",
        "    multiple sentence embeddings into a single embedding?\"\"\",\n",
        "    \"\"\"Could you explain this process step by step from start to finish? Explain\n",
        "    like I'm very new to this space, assume I don't have much prior knowledge\n",
        "    of embeddings, neural nets, etc\"\"\",\n",
        "    \"\"\"Awesome thanks! Are there any popular 'heirarchical neural network'\n",
        "    models that I can look up? Or maybe just the second stage that creates the\n",
        "    hierarchical embeddings?\"\"\",\n",
        "    \"It seems like these HAN models are quite old, is there anything more recent?\",\n",
        "    \"Can you explain the difference between transformer-XL and longformer?\",\n",
        "    \"How much text can be encoded by each of these models?\",\n",
        "    \"\"\"Okay very interesting, so before returning to earlier in the conversation.\n",
        "    I understand now that there are a lot of different transformer (and not\n",
        "    transformer) based models for creating the embeddings from vectors. Is that\n",
        "    correct?\"\"\",\n",
        "    \"\"\"Perfect, so I understand text can be encoded into these embeddings. But\n",
        "    what then? Once I have my embeddings what do I do?\"\"\",\n",
        "    \"\"\"I'd like to use these embeddings to help a chatbot or a question-answering\n",
        "    system answer questions with help from this external knowledge base. I\n",
        "    suppose this would come under information retrieval? Could you explain that\n",
        "    process in a little more detail?\"\"\",\n",
        "    \"\"\"Okay great, that sounds like what I'm hoping to do. When you say the\n",
        "    'chatbot or question-answering system generates an embedding', what do you\n",
        "    mean exactly?\"\"\",\n",
        "    \"\"\"Ah okay, I understand, so it isn't the 'chatbot' model specifically\n",
        "    creating the embedding right? That's how I understood your earlier comment.\n",
        "    It seems more like there is a separate embedding model? And that encodes\n",
        "    the query, then we retrieve the set of relevant documents from the\n",
        "    external knowledge base? How is that information then used by the chatbot\n",
        "    or question-answering system exactly?\"\"\",\n",
        "    \"\"\"Okay but how is the information provided to the chatbot or\n",
        "    question-answering system?\"\"\",\n",
        "    \"\"\"So the retrieved information is given to the chatbot / QA system as plain\n",
        "    text? But then how do we pass in the original query? How can the system\n",
        "    distinguish between a user's query and all of this additional information?\"\"\",\n",
        "    \"\"\"That doesn't seem correct to me, my question is — if we are giving the\n",
        "    chatbot / QA system the user's query AND retrieved information from an\n",
        "    external knowledge base, and it's all fed into the model as plain text,\n",
        "    how does the model know what part of the plain text is a query vs. retrieved\n",
        "    information?\"\"\",\n",
        "    \"\"\"Yes I get that, but in the text passed to the model, how do we identify\n",
        "    user prompt vs retrieved information?\"\"\"\n",
        "\n",
        "]\n",
        "\n",
        "def talk(conversation_chain):\n",
        "    tokens_used = []\n",
        "    # we loop through the conversation above, counting token usage as we go\n",
        "    for user_query in tqdm(queries):\n",
        "        try:\n",
        "            # Get the history factory function name\n",
        "            history_factory = conversation_chain.get_session_history.__name__\n",
        "\n",
        "            # Create appropriate config based on history factory type\n",
        "            if history_factory == \"get_summary_chat_history\":\n",
        "                config = {\"configurable\": {\"session_id\": \"summary_history\", \"llm\": llm}}\n",
        "            elif history_factory in [\"get_window_chat_history_k6\", \"get_window_chat_history_k12\"]:\n",
        "                k = 6 if \"k6\" in history_factory else 12\n",
        "                config = {\"configurable\": {\"session_id\": f\"window_history_k{k}\", \"k\": k}}\n",
        "            elif history_factory in [\"get_summary_buffer_chat_history_k6\", \"get_summary_buffer_chat_history_k12\"]:\n",
        "                k = 6 if \"k6\" in history_factory else 12\n",
        "                config = {\"configurable\": {\"session_id\": f\"summary_buffer_k{k}\", \"llm\": llm, \"k\": k}}\n",
        "            else:\n",
        "                config = {\"configurable\": {\"session_id\": \"basic_history\"}}\n",
        "\n",
        "            res = count_tokens(\n",
        "                conversation_chain,\n",
        "                user_query,\n",
        "                config=config\n",
        "            )\n",
        "            tokens_used.append(res['token_count'])\n",
        "        except (openai.APIError, openai.APIConnectionError, openai.RateLimitError, openai.APIStatusError) as e:\n",
        "            # we hit the token limit of the model or another API error, so break\n",
        "            print(f\"Hit error: {e}\")\n",
        "            break\n",
        "    return tokens_used"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5DcHVsn2L9cy"
      },
      "source": [
        "## Run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "Rxirb9Vt5oOj",
        "outputId": "4a7b2c18-81be-4c0d-ec53-c7094695a577"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RunnableWithMessageHistory\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "daab3b49ad8740d59df0994db74a57a3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/27 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Spent a total of 74 tokens\n",
            "Spent a total of 629 tokens\n",
            "Spent a total of 1671 tokens\n",
            "Spent a total of 2429 tokens\n",
            "Spent a total of 3476 tokens\n",
            "Spent a total of 4186 tokens\n",
            "Spent a total of 5057 tokens\n",
            "Spent a total of 5765 tokens\n",
            "Spent a total of 6460 tokens\n",
            "Spent a total of 7142 tokens\n",
            "Spent a total of 7726 tokens\n",
            "Spent a total of 8516 tokens\n",
            "Spent a total of 9216 tokens\n",
            "Spent a total of 10228 tokens\n",
            "Spent a total of 11128 tokens\n",
            "Spent a total of 12060 tokens\n",
            "Spent a total of 13096 tokens\n",
            "Spent a total of 13743 tokens\n",
            "Spent a total of 14353 tokens\n",
            "Spent a total of 15006 tokens\n",
            "Spent a total of 15892 tokens\n",
            "Spent a total of 16559 tokens\n",
            "Spent a total of 17330 tokens\n",
            "Spent a total of 18028 tokens\n",
            "Spent a total of 18610 tokens\n",
            "Spent a total of 19295 tokens\n",
            "Spent a total of 19813 tokens\n",
            "ConversationSummaryMessageHistory\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "32ba0d9d4d89486ba2376f1b37019145",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/27 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Spent a total of 225 tokens\n",
            "Spent a total of 1643 tokens\n",
            "Spent a total of 3250 tokens\n",
            "Spent a total of 3180 tokens\n",
            "Spent a total of 4582 tokens\n",
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            "Spent a total of 1950 tokens\n",
            "Spent a total of 1338 tokens\n",
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            "Spent a total of 2732 tokens\n",
            "Spent a total of 4310 tokens\n",
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            "Spent a total of 2490 tokens\n",
            "Spent a total of 3024 tokens\n",
            "Spent a total of 4575 tokens\n",
            "Spent a total of 4727 tokens\n",
            "Spent a total of 3096 tokens\n",
            "Spent a total of 2633 tokens\n",
            "Spent a total of 2523 tokens\n",
            "Spent a total of 2492 tokens\n",
            "Spent a total of 2615 tokens\n",
            "Spent a total of 2336 tokens\n",
            "BufferWindowMessageHistory(k=6)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f639f24ecac54cb4bffa10753f85e88c",
              "version_major": 2,
              "version_minor": 0
            },
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Initializing BufferWindowMessageHistory with k=6\n",
            "Spent a total of 74 tokens\n",
            "Initializing BufferWindowMessageHistory with k=6\n",
            "Spent a total of 511 tokens\n",
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            "Initializing BufferWindowMessageHistory with k=6\n",
            "Spent a total of 586 tokens\n",
            "Initializing BufferWindowMessageHistory with k=6\n",
            "Spent a total of 595 tokens\n",
            "Initializing BufferWindowMessageHistory with k=6\n",
            "Spent a total of 657 tokens\n",
            "Initializing BufferWindowMessageHistory with k=6\n",
            "Spent a total of 501 tokens\n",
            "Initializing BufferWindowMessageHistory with k=6\n",
            "Spent a total of 552 tokens\n",
            "Initializing BufferWindowMessageHistory with k=6\n",
            "Spent a total of 435 tokens\n",
            "Initializing BufferWindowMessageHistory with k=6\n",
            "Spent a total of 529 tokens\n",
            "Initializing BufferWindowMessageHistory with k=6\n",
            "Spent a total of 508 tokens\n",
            "Initializing BufferWindowMessageHistory with k=6\n",
            "Spent a total of 583 tokens\n",
            "BufferWindowMessageHistory(k=12)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "63496596987f4794859adc454a43f7eb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/27 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 74 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 461 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 148 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 506 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 717 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 548 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 952 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 501 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 582 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 403 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 303 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 586 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 566 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 663 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 748 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 389 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 681 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 133 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 601 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 495 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 641 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 543 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 529 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 426 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 425 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 547 tokens\n",
            "Initializing BufferWindowMessageHistory with k=12\n",
            "Spent a total of 602 tokens\n",
            "ConversationSummaryBufferMessageHistory(k=6)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "481d6a256c394b7693f7640c01e0ec09",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/27 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Spent a total of 74 tokens\n",
            "Spent a total of 565 tokens\n",
            "Spent a total of 188 tokens\n",
            "Spent a total of 431 tokens\n",
            "Spent a total of 623 tokens\n",
            "Spent a total of 508 tokens\n",
            "Spent a total of 798 tokens\n",
            "Spent a total of 362 tokens\n",
            "Spent a total of 594 tokens\n",
            "Spent a total of 435 tokens\n",
            "Spent a total of 309 tokens\n",
            "Spent a total of 651 tokens\n",
            "Spent a total of 627 tokens\n",
            "Spent a total of 950 tokens\n",
            "Spent a total of 751 tokens\n",
            "Spent a total of 398 tokens\n",
            "Spent a total of 750 tokens\n",
            "Spent a total of 122 tokens\n",
            "Spent a total of 613 tokens\n",
            "Spent a total of 560 tokens\n",
            "Spent a total of 677 tokens\n",
            "Spent a total of 473 tokens\n",
            "Spent a total of 526 tokens\n",
            "Spent a total of 461 tokens\n",
            "Spent a total of 557 tokens\n",
            "Spent a total of 534 tokens\n",
            "Spent a total of 646 tokens\n",
            "ConversationSummaryBufferMessageHistory(k=12)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e33a510efbaa4a02a946600142c78e0b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/27 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Spent a total of 74 tokens\n",
            "Spent a total of 488 tokens\n",
            "Spent a total of 276 tokens\n",
            "Spent a total of 550 tokens\n",
            "Spent a total of 732 tokens\n",
            "Spent a total of 503 tokens\n",
            "Spent a total of 1008 tokens\n",
            "Spent a total of 305 tokens\n",
            "Spent a total of 579 tokens\n",
            "Spent a total of 408 tokens\n",
            "Spent a total of 319 tokens\n",
            "Spent a total of 640 tokens\n",
            "Spent a total of 591 tokens\n",
            "Spent a total of 790 tokens\n",
            "Spent a total of 736 tokens\n",
            "Spent a total of 467 tokens\n",
            "Spent a total of 721 tokens\n",
            "Spent a total of 129 tokens\n",
            "Spent a total of 598 tokens\n",
            "Spent a total of 498 tokens\n",
            "Spent a total of 651 tokens\n",
            "Spent a total of 477 tokens\n",
            "Spent a total of 493 tokens\n",
            "Spent a total of 481 tokens\n",
            "Spent a total of 511 tokens\n",
            "Spent a total of 531 tokens\n",
            "Spent a total of 590 tokens\n"
          ]
        }
      ],
      "source": [
        "counts = {}\n",
        "# loop through each of our memory types above\n",
        "for key, chain in conversation_chains.items():\n",
        "    print(key)\n",
        "    counts[key] = talk(chain)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UFFYlgLiL9cy"
      },
      "source": [
        "## Plot Token Usage for Various Memory Setups\n",
        "\n",
        "Here we can see relative token usage of different memory types."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "HMRC22_z72kr",
        "outputId": "8dfa241e-311c-4033-c30d-04abcc217f94"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "max_tokens = 4096\n",
        "\n",
        "colors = [\"#1c17ff\", \"#738FAB\", \"#f77f00\", \"#fcbf49\", \"#38c172\", \"#4dc0b5\"]\n",
        "\n",
        "for i, (key, count) in enumerate(counts.items()):\n",
        "    color = colors[i]\n",
        "    sns.lineplot(\n",
        "        x=range(1, len(count)+1),\n",
        "        y=count,\n",
        "        label=key,\n",
        "        color=color\n",
        "    )\n",
        "    if max_tokens in count:\n",
        "        plt.plot(\n",
        "            len(count), max_tokens, marker=\"X\", color=\"red\", markersize=10\n",
        "        )\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r19MnfM3L9cy"
      },
      "source": [
        "Or, alternatively, a logarithmic plot to show the non `RunnableWithMessageHistory` (blue line) plots more clearly."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "d5vptF9SL9cy",
        "outputId": "a93093a8-c303-4b93-813b-fa3a494126e0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "max_tokens = 4096\n",
        "\n",
        "colors = [\"#1c17ff\", \"#738FAB\", \"#f77f00\", \"#fcbf49\", \"#38c172\", \"#4dc0b5\"]\n",
        "\n",
        "for i, (key, count) in enumerate(counts.items()):\n",
        "    color = colors[i]\n",
        "    sns.lineplot(\n",
        "        x=range(1, len(count)+1),\n",
        "        y=count,\n",
        "        label=key,\n",
        "        color=color\n",
        "    )\n",
        "    if max_tokens in count:\n",
        "        plt.plot(\n",
        "            len(count), max_tokens, marker=\"X\", color=\"red\", markersize=10\n",
        "        )\n",
        "\n",
        "plt.yscale('log')  # Set y-axis to logarithmic scale\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jZuSLqremRX0"
      },
      "source": [
        "---"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "pinecone1",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
